Production line optimization method and system based on digital twinning

Abstract

The invention discloses a production line optimization method and system based on digital twinning, and the method comprises the steps: carrying out real-time simulation according to a production line simulation model, obtaining a real-time simulation result, and dynamically adjusting production control parameters through a preset parameter optimization strategy according to the simulation result and a preset production optimization target; the optimized control parameters of the at least one production section are applied to the digital twin model, and the optimization effect is predicted and evaluated; and after the effect reaches the expectation, the optimized production control parameters are applied to the physical production line. The production control parameters are dynamically adjusted through the production line data, the production control parameters can be dynamically adjusted according to the three production optimization targets of the minimum takt time, the minimum load fluctuation between workstations and the minimum adjustment cost, and the accuracy and effectiveness of adjustment of the production control parameters are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent production lines, and particularly relates to a production line optimization method and system based on digital twin. Background Art

[0002] Industrial simulation is a virtualization of physical industry, which transforms each module in the physical industry into data and integrates it into a virtual system. In this system, each work and process in the industrial process is simulated and various interactions are realized. Although industrial simulation has been widely applied by many enterprises to various links of industry, it has played an important role in improving the development efficiency of enterprises, strengthening the data collection, analysis, and processing capabilities, reducing decision-making errors, and reducing enterprise risks.

[0003] However, currently, the optimization of production lines can often only solve local or short-term optimization problems, and it is difficult to achieve global and long-term production efficiency improvement and cost reduction. Summary of the Invention

[0004] The present invention provides a production line optimization method and system based on digital twin, which is used to solve the technical problem that it is difficult to achieve global and long-term production efficiency improvement and cost reduction.

[0005] In a first aspect, the present invention provides a production line optimization method based on digital twin, including:

[0006] Obtaining production line data of a physical production line at the current moment, and constructing a production line simulation model of the physical production line by using digital twin according to the production line data, wherein the production line data includes control parameter data and physical equipment parameter data;

[0007] Performing real-time simulation according to the production line simulation model to obtain a real-time simulation result, and dynamically adjusting production control parameters through a preset parameter optimization strategy according to the simulation result and a preset production optimization target, wherein the production control parameters include control parameters of at least one production section;

[0008] Applying the optimized control parameters of at least one production section to the digital twin model to predict and evaluate the optimization effect; when the effect meets the expectation, applying the optimized production control parameters to the physical production line.

[0009] In a second aspect, the present invention provides a production line optimization system based on digital twin, including:

[0010] An acquisition module configured to obtain production line data of a physical production line at the current moment, and construct a production line simulation model of the physical production line by using digital twin according to the production line data, wherein the production line data includes control parameter data and physical equipment parameter data;

[0011] An adjustment module, configured to perform real-time simulation according to the production line simulation model to obtain real-time simulation results, and dynamically adjust production control parameters according to the simulation results and a preset production optimization target through a preset parameter optimization strategy, wherein the production control parameters include control parameters of at least one production section;

[0012] An output module, configured to apply the optimized control parameters of at least one production section to the digital twin model to predict and evaluate the optimization effect; when the effect meets the expectation, apply the optimized production control parameters to the physical production line.

[0013] In a third aspect, an electronic device is provided, which includes: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the steps of the method for optimizing a production line based on digital twin according to any embodiment of the present invention.

[0014] In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the program instructions are executed by a processor, the processor is enabled to execute the steps of the method for optimizing a production line based on digital twin according to any embodiment of the present invention.

[0015] For the method and system for optimizing a production line based on digital twin of the present application, the digital twin model is constructed by collecting production line data on the physical production line, making the model more in line with the actual situation and improving the accuracy of prediction. By dynamically adjusting production control parameters through production line data, it is possible to dynamically adjust production control parameters for the three production optimization targets of minimum cycle time, minimizing load fluctuations between workstations, and minimizing adjustment costs, improving the accuracy and effectiveness of the adjustment of production control parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0017] Figure 1 It is a flowchart of a method for optimizing a production line based on digital twin provided by an embodiment of the present invention;

[0018] Figure 2 It is a structural block diagram of a system for optimizing a production line based on digital twin provided by an embodiment of the present invention;

[0019] Figure 3 It is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. Detailed implementation manners

[0020] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0021] Please refer to Figure 1 , which shows a flowchart of a production line optimization method based on digital twin of the present application.

[0022] As Figure 1 shown, the production line optimization method based on digital twin specifically includes the following steps:

[0023] Step S101: Obtain the production line data of the physical production line at the current moment, and according to the production line data, use digital twin to construct a production line simulation model of the physical production line. Among them, the production line data includes control parameter data and physical device parameter data.

[0024] In this step, establish a process data model for each production process of the physical production line according to the production line data, and establish the production line simulation model based on the influence relationship between the production processes of the physical production line.

[0025] Step S102: Perform real-time simulation according to the production line simulation model to obtain a real-time simulation result, and according to the simulation result and a preset production optimization target, dynamically adjust the production control parameters through a preset parameter optimization strategy. Among them, the production control parameters include control parameters of at least one production section.

[0026] In this step, write a simulation program, and the simulation program includes simulation parameters; perform at least one simulation test according to the simulation program, and the simulation parameters are not completely the same in each simulation test; when performing the simulation test, obtain the monitoring image of the simulation test, and judge the simulation effect of the production line simulation model according to the controlled image to obtain an optimization plan; use the optimization plan to adjust the simulation parameters to perform the next simulation test; after the simulation test is completed, obtain the corresponding simulation result.

[0027] Step S103: Apply the control parameters of at least one optimized production section to the digital twin model to predict and evaluate the optimization effect; when the effect meets the expectation, apply the optimized production control parameters to the physical production line.

[0028] In this step, according to the simulation results and the preset production optimization objectives, the production control parameters are dynamically adjusted with the minimum cycle time, minimizing the load fluctuation between workstations, and minimizing the adjustment cost as the objective functions. Among them, the expression of the objective function is:

[0029] f = minT + minLF + minC,

[0030] In the formula, f is the objective function, T is the cycle time, LF is the load fluctuation between workstations, and C is the adjustment cost.

[0031] It should be noted that the expression for calculating the minimum cycle time is:

[0032]

[0033] In the formula, N is the total number of workstations, t i is the working time of the i-th task, x in is the i-th task assigned to the n-th workstation;

[0034] The expression for calculating the minimum load fluctuation between workstations is:

[0035]

[0036] In the formula, M is the total number of tasks;

[0037] The expression for calculating the minimum adjustment cost is:

[0038]

[0039] In the formula, t ip is the time for the p-th device to complete the i-th task, y np is the cost for the p-th device assigned to the n-th workstation.

[0040] In summary, in the method of the present application, the digital twin model is constructed by collecting production line data on the physical production line, making the model more in line with the actual situation and improving the accuracy of prediction. By dynamically adjusting production control parameters through production line data, it is possible to dynamically adjust production control parameters for the three production optimization objectives of minimum cycle time, minimizing the load fluctuation between workstations, and minimizing the adjustment cost, improving the accuracy and effectiveness of the adjustment of production control parameters.

[0041] In a specific embodiment, a production line optimization system based on digital twin is constructed, specifically including: Data acquisition layer: used to collect real-time data on the production line, including equipment status data, production environment data, product quality data, etc. Digital twin layer: used to construct a digital twin model of the production line to achieve real-time mapping and data interaction between the physical production line and the virtual model. Data analysis layer: used to preprocess, extract features and analyze the collected data, and construct prediction models and optimization models using deep learning algorithms. Decision and control layer: used to generate optimization decisions based on the analysis results and control the production line to perform corresponding operations. Human-machine interaction layer: used to provide a visual interface to facilitate users to monitor the production line status, view analysis results and set parameters.

[0042] The system of this embodiment can realize the real-time mapping between the physical production line and the virtual model through digital twin technology, can timely detect abnormal situations in the production process and make adjustments, and can generate optimization decisions based on the analysis results, which can improve production efficiency, reduce production costs, and improve product quality. Moreover, the system can be flexibly configured according to different production requirements to meet the needs of personalized customization.

[0043] Please refer to Figure 2 , which shows the structural block diagram of a production line optimization system based on digital twin of the present application.

[0044] As Figure 2 shown, the production line optimization system 200 includes an acquisition module 210, an adjustment module 220, and an output module 230.

[0045] Among them, the acquisition module 210 is configured to acquire the production line data of the physical production line at the current moment, and according to the production line data, construct a production line simulation model of the physical production line using digital twin, where the production line data includes control parameter data and physical equipment parameter data; the adjustment module 220 is configured to perform real-time simulation according to the production line simulation model to obtain a real-time simulation result, and according to the simulation result and a preset production optimization target, dynamically adjust the production control parameters through a preset parameter optimization strategy, where the production control parameters include the control parameters of at least one production section; the output module 230 is configured to apply the optimized control parameters of at least one production section to the digital twin model to predict and evaluate the optimization effect; when the effect reaches the expectation, apply the optimized production control parameters to the physical production line.

[0046] It should be understood that Figure 2 the modules described in Figure 1 correspond to the respective steps in the method described with reference to Figure 2 . Therefore, the operations, features and corresponding technical effects described above for the method also apply to the Figure 2 modules, and will not be elaborated here.

[0047] In some other embodiments, the embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored. When the program instructions are executed by a processor, the processor is caused to execute the digital-twin-based production line optimization method in any of the above method embodiments;

[0048] As an implementation, the computer-readable storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are set as follows:

[0049] Obtain the production line data of the physical production line at the current moment, and according to the production line data, use digital twin to construct a production line simulation model of the physical production line, wherein the production line data includes control parameter data and physical device parameter data;

[0050] Perform real-time simulation according to the production line simulation model to obtain a real-time simulation result, and according to the simulation result and a preset production optimization target, dynamically adjust production control parameters through a preset parameter optimization strategy, wherein the production control parameters include control parameters of at least one production section;

[0051] Apply the optimized control parameters of at least one production section to the digital twin model to predict and evaluate the optimization effect; when the effect meets the expectation, apply the optimized production control parameters to the physical production line.

[0052] The computer-readable storage medium may include a program storage area and a data storage area. Among them, the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created according to the use of the digital-twin-based production line optimization system, etc. In addition, the computer-readable storage medium may include a high-speed random access memory, and may also include a memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some embodiments, the computer-readable storage medium may optionally include a memory remotely set relative to the processor, and these remote memories may be connected to the digital-twin-based production line optimization system through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0053] Figure 3 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention, as Figure 3 shown. The device includes: a processor 310 and a memory 320. The electronic device may further include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330, and the output device 340 may be connected through a bus or other means, Figure 3Take the bus connection as an example. The memory 320 is the computer-readable storage medium described above. The processor 310 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions, and modules stored in the memory 320, that is, implements the digital-twin-based production line optimization method in the above method embodiment. The input device 330 can receive input digital or character information, and generate key signal inputs related to the user settings and function controls of the digital-twin-based production line optimization system. The output device 340 can include display devices such as a display screen.

[0054] The above electronic device can execute the method provided by the embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. For the technical details not described in detail in this embodiment, reference can be made to the method provided by the embodiment of the present invention.

[0055] As an implementation manner, the above electronic device is applied to a digital-twin-based production line optimization system and is used for a client, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to:

[0056] Obtain the production line data of the physical production line at the current moment, and according to the production line data, use digital twin to construct a production line simulation model of the physical production line, wherein the production line data includes control parameter data and physical device parameter data;

[0057] Perform real-time simulation according to the production line simulation model to obtain real-time simulation results, and according to the simulation results and a preset production optimization target, dynamically adjust production control parameters through a preset parameter optimization strategy, wherein the production control parameters include the control parameters of at least one production section;

[0058] Apply the optimized control parameters of at least one production section to the digital twin model to predict and evaluate the optimization effect; when the effect meets the expectation, apply the optimized production control parameters to the physical production line.

[0059] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiments.

[0060] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A production line optimization method based on digital twin, characterized in that, Including: Obtain the production line data of the physical production line at the current moment, and based on the production line data, use digital twin to construct a production line simulation model of the physical production line, where the production line data includes control parameter data and physical equipment parameter data; Perform real-time simulation according to the production line simulation model to obtain real-time simulation results, and dynamically adjust the production control parameters through a preset parameter optimization strategy according to the simulation results and a preset production optimization target, where the production control parameters include the control parameters of at least one production section; Apply the optimized control parameters of at least one production section to the digital twin model to predict and evaluate the optimization effect; when the effect meets the expectation, apply the optimized production control parameters to the physical production line.

2. The optimization method of a production line based on digital twin according to claim 1, wherein, The constructing a production line simulation model of the physical production line using digital twin according to the production line data includes: Establish a process data model for each production process of the physical production line according to the production line data, and establish the production line simulation model based on the influence relationship between the production processes of the physical production line.

3. The optimization method for a production line based on digital twin according to claim 1, characterized in that, The performing real-time simulation according to the production line simulation model to obtain real-time simulation results includes: Write a simulation program, where the simulation program includes simulation parameters; Perform at least one simulation test according to the simulation program, and the simulation parameters are not completely the same in each simulation test; When performing the simulation test, obtain the monitoring image of the simulation test, and judge the simulation effect of the production line simulation model according to the monitoring image to obtain an optimization plan; Use the optimization plan to adjust the simulation parameters for the next simulation test; Obtain the corresponding simulation results after the simulation test ends.

4. The optimization method of a production line based on digital twin according to claim 1, wherein, The dynamically adjusting the production control parameters through a preset parameter optimization strategy according to the simulation results and a preset production optimization target includes: Dynamically adjust the production control parameters with the minimum cycle time, minimizing the load fluctuation between workstations, and minimizing the adjustment cost as the objective function according to the simulation results and a preset production optimization target, where the expression of the objective function is: f = minT + minLF + minC, In the formula, f is the objective function, T is the cycle time, LF is the load fluctuation between workstations, and C is the adjustment cost.

5. The optimization method of a production line based on digital twin according to claim 4, wherein, Wherein, The expression for calculating the minimum cycle time is: Where N is the total number of workstations, and t i is the working time of the i-th task, and x in is the i-th task assigned to the n-th workstation; The expression for calculating the minimizing the load fluctuation between workstations is: In the formula, M is the total number of tasks; The expression for calculating the minimizing the adjustment cost is: where t ip is the time for the p-th device to complete the i-th task, and y np is the cost for the p-th device to be assigned to the n-th workstation.

6. An optimization system for a production line based on digital twin, characterized in that, Including: An acquisition module configured to obtain the production line data of the physical production line at the current moment, and based on the production line data, use digital twin to construct a production line simulation model of the physical production line, where the production line data includes control parameter data and physical equipment parameter data; An adjustment module configured to perform real-time simulation according to the production line simulation model to obtain real-time simulation results, and dynamically adjust the production control parameters through a preset parameter optimization strategy according to the simulation results and a preset production optimization target, where the production control parameters include the control parameters of at least one production section; An output module, configured to apply the optimized control parameters of at least one production section to the digital twin model, predict and evaluate the optimization effect; when the effect meets the expectation, apply the optimized production control parameters to the physical production line.

7. An electronic device, characterized in that, It includes: At least one processor and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, the method according to any one of claims 1 to 5 is implemented.

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